--- title: "Using a delay-adjusted case fatality ratio to estimate under-reporting" description: "Using a corrected case fatality ratio, we calculate estimates of the level of under-reporting for any country with greater than ten deaths" status: real-time-report rmarkdown_html_fragment: true update: 2020-06-21 authors: - id: tim_russell corresponding: true - id: joel_hellewell equal: 1 - id: sam_abbott equal: 1 - id: nick_golding - id: hamish_gibbs - id: chris_jarvis - id: kevin_vanzandvoort - id: ncov-group - id: stefan_flasche - id: roz_eggo - id: john_edmunds - id: adam_kucharski ---

Aim

To estimate the percentage of symptomatic COVID-19 cases reported in different countries using case fatality ratio estimates based on data from the ECDC, correcting for delays between confirmation-and-death.

Data availability

The under-reporting estimates for all countries can be downloaded as a single .csv file here.

Similarly, the prevalence estimates can be downloaded as a single .csv file here.

Methods Summary

Current estimates of under-reporting, prevalence and adjusted case curves along with reported cases

Temporal variation

Figure 1: Temporal variation in reporting rate. We calculate the percentage of symptomatic cases reported on each day a country has had more than ten deaths. We then fit a Gaussian Process (GP) to these data (see Temporal variation model fitting section for details), highlighting the temporal trend of each countries reporting rate. The red shaded region is the 95% CrI of fitted GP.

Prevalence estimates

Country Prevalence median (95% CrI) Total reported cases New reported cases (tallied over last 10 days) Population
Afghanistan 0.11% (0.05% - 0.26%) 22,890 9,231 38,928,341
Albania 0.023% (0.013% - 0.075%) 1,385 286 2,877,800
Algeria 0.027% (0.013% - 0.066%) 10,589 1,455 43,851,043
Andorra 0.69% (0.21% - 2.3%) 852 88 77,265
Argentina 0.12% (0.059% - 0.26%) 27,360 11,954 45,195,777
Armenia 0.77% (0.39% - 1.7%) 14,669 5,993 2,963,234
Australia 0.00097% (0.00057% - 0.0022%) 7,285 112 25,499,881
Austria 0.016% (0.0058% - 0.046%) 16,964 370 9,006,400
Azerbaijan 0.099% (0.051% - 0.23%) 8,882 3,893 10,139,175
Bahamas 0.00085% (0.00033% - 0.0049%) 103 1 393,248
Bahrain 0.82% (0.52% - 1.5%) 17,269 6,820 1,701,583
Bangladesh 0.055% (0.028% - 0.12%) 78,052 35,208 164,689,383
Belarus 0.24% (0.15% - 0.45%) 51,816 11,052 9,449,321
Belgium 0.086% (0.044% - 0.2%) 59,711 1,650 11,589,616
Bolivia 0.36% (0.19% - 0.77%) 16,165 7,434 11,673,029
Bosnia and Herzegovina 0.04% (0.015% - 0.16%) 2,831 346 3,280,815
Brazil 1.1% (0.61% - 2.3%) 802,828 337,662 212,559,409
Bulgaria 0.085% (0.04% - 0.22%) 3,086 587 6,948,445
Burkina Faso 0.00077% (0.00029% - 0.0029%) 892 45 20,903,278
Cameroon 0.026% (0.016% - 0.053%) 8,681 3,245 26,545,864
Canada 0.2% (0.11% - 0.42%) 97,519 8,112 37,742,157
Chad 0.0019% (0.00075% - 0.0083%) 848 89 16,425,859
Chile 0.71% (0.43% - 3.8%) 154,092 63,454 19,116,209
China 2.9e-05% (1e-05% - 0.00015%) 84,216 93 1,439,323,774
Colombia 0.19% (0.1% - 0.41%) 43,682 16,994 50,882,884
Congo 0.013% (0.0048% - 0.041%) 745 158 5,518,092
Costa Rica 0.024% (0.013% - 0.064%) 1,538 516 5,094,114
Côte d’Ivoire 0.014% (0.0081% - 0.029%) 4,404 1,654 26,378,275
Croatia 0.00089% (3e-04% - 0.0035%) 2,249 4 4,105,268
Cuba 0.0048% (0.0025% - 0.014%) 2,219 214 11,326,616
Cyprus 0.0068% (0.0037% - 0.021%) 975 34 1,207,361
Czechia 0.021% (0.0088% - 0.055%) 9,886 690 10,708,982
Democratic Republic of the Congo 0.005% (0.0024% - 0.015%) 4,514 1,681 89,561,404
Denmark 0.026% (0.012% - 0.069%) 12,035 442 5,792,203
Djibouti 0.33% (0.2% - 0.73%) 4,398 1,484 988,002
Dominican Republic 0.1% (0.059% - 0.22%) 21,437 4,906 10,847,904
Ecuador 0.25% (0.13% - 0.54%) 44,440 5,869 17,643,060
Egypt 0.091% (0.048% - 0.19%) 39,726 17,644 102,334,403
El Salvador 0.067% (0.031% - 0.16%) 3,481 1,203 6,486,201
Equatorial Guinea 0.042% (0.024% - 0.095%) 1,306 263 1,402,985
Estonia 0.041% (0.015% - 0.11%) 1,965 106 1,326,539
Ethiopia 0.0072% (0.0031% - 0.018%) 2,670 1,702 114,963,583
Finland 0.014% (0.0068% - 0.04%) 7,064 288 5,540,718
France 0.052% (0.028% - 0.11%) 155,561 5,893 65,273,512
Gabon 0.08% (0.05% - 0.16%) 3,463 850 2,225,728
Georgia 0.0054% (0.0028% - 0.016%) 831 85 3,989,175
Germany 0.03% (0.015% - 0.064%) 185,674 4,478 83,783,945
Ghana 0.018% (0.011% - 0.033%) 10,358 2,742 31,072,945
Greece 0.015% (0.0063% - 0.043%) 3,088 179 10,423,056
Guatemala 0.28% (0.13% - 0.66%) 8,561 3,954 17,915,567
Guinea 0.011% (0.0071% - 0.022%) 4,372 716 13,132,792
Guyana 0.0038% (0.0013% - 0.021%) 158 8 786,559
Haiti 0.049% (0.027% - 0.12%) 3,941 2,357 11,402,533
Honduras 0.19% (0.085% - 0.47%) 7,669 2,783 9,904,608
Hungary 0.029% (0.013% - 0.072%) 4,039 198 9,660,350
Iceland 0.0014% (0.00076% - 0.004%) 1,807 2 341,250
India 0.057% (0.031% - 0.12%) 297,535 123,772 1,380,004,385
Indonesia 0.033% (0.017% - 0.068%) 35,295 10,079 273,523,621
Iran 0.18% (0.094% - 0.36%) 180,176 33,508 83,992,953
Iraq 0.21% (0.11% - 0.45%) 16,675 10,802 40,222,503
Ireland 0.08% (0.034% - 0.22%) 25,238 362 4,937,796
Isle of Man 0% (0% - 0%) 336 0 85,032
Israel 0.049% (0.026% - 0.11%) 18,701 1,714 8,655,541
Italy 0.11% (0.057% - 0.22%) 236,142 3,894 60,461,828
Japan 0.0026% (0.0012% - 0.0065%) 17,332 528 126,476,458
Kazakhstan 0.038% (0.024% - 0.074%) 13,872 3,490 18,776,707
Kenya 0.011% (0.0048% - 0.029%) 3,215 1,470 53,771,300
Kosovo 0.042% (0.02% - 0.12%) 1,326 278 1,810,366
Kuwait 0.45% (0.28% - 0.82%) 34,432 9,248 4,270,563
Kyrgyzstan 0.016% (0.0088% - 0.041%) 2,166 444 6,524,191
Latvia 0.0068% (0.0025% - 0.023%) 1,094 30 1,886,202
Lebanon 0.0087% (0.0044% - 0.027%) 1,402 230 6,825,442
Liberia 0.021% (0.0059% - 0.081%) 410 137 5,057,677
Lithuania 0.023% (0.0096% - 0.076%) 1,752 90 2,722,291
Luxembourg 0.023% (0.01% - 0.061%) 4,052 40 625,976
Malaysia 0.0041% (0.0026% - 0.0082%) 8,369 637 32,365,998
Mali 0.023% (0.01% - 0.054%) 1,722 496 20,250,834
Mauritania 0.18% (0.072% - 0.48%) 1,162 739 4,649,660
Mauritius 5e-04% (2e-04% - 0.003%) 337 2 1,271,767
Mexico 0.94% (0.51% - 1.9%) 133,974 49,347 128,932,753
Moldova 0.44% (0.22% - 0.94%) 10,727 2,831 4,033,963
Morocco 0.0046% (0.0029% - 0.0099%) 8,537 823 36,910,558
Netherlands 0.075% (0.036% - 0.17%) 48,251 2,125 17,134,873
New Zealand 0% (0% - 0%) 1,154 0 4,822,233
Nicaragua 0.031% (0.014% - 0.18%) 1,464 705 6,624,554
Niger 0.00049% (0.00013% - 0.002%) 974 19 24,206,636
Nigeria 0.01% (0.0051% - 0.023%) 14,554 5,252 206,139,587
North Macedonia 0.63% (0.3% - 1.4%) 3,542 1,412 2,083,380
Norway 0.0092% (0.0044% - 0.039%) 8,594 183 5,421,242
Oman 0.41% (0.26% - 0.74%) 19,954 10,134 5,106,622
Pakistan 0.12% (0.063% - 0.24%) 125,933 59,476 220,892,331
Panama 0.76% (0.38% - 1.7%) 18,586 6,055 4,314,768
Paraguay 0.0094% (0.0057% - 0.02%) 1,230 313 7,132,530
Peru 0.93% (0.5% - 1.9%) 214,788 66,503 32,971,846
Philippines 0.018% (0.0099% - 0.038%) 24,175 7,541 109,581,085
Poland 0.067% (0.032% - 0.16%) 28,201 5,046 37,846,605
Portugal 0.19% (0.093% - 0.43%) 35,910 3,964 10,196,707
Puerto Rico 0.13% (0.077% - 0.28%) 5,352 1,705 2,860,840
Qatar 1.9% (1% - 10%) 75,071 22,164 2,881,060
Romania 0.085% (0.043% - 0.2%) 21,182 2,200 19,237,682
Russia 0.25% (0.15% - 0.49%) 502,436 114,813 145,934,460
San Marino 0.14% (0.077% - 0.59%) 691 20 33,938
Sao Tome and Principe 0.2% (0.1% - 0.62%) 639 176 219,161
Saudi Arabia 0.49% (0.25% - 1%) 116,021 34,255 34,813,867
Senegal 0.018% (0.01% - 0.041%) 4,759 1,330 16,743,930
Serbia 0.018% (0.011% - 0.042%) 12,102 748 8,737,370
Sierra Leone 0.01% (0.0044% - 0.032%) 1,085 256 7,976,985
Singapore 0.21% (0.12% - 0.62%) 39,387 5,527 5,850,343
Sint Maarten 0% (0% - 0%) 77 0 42,882
Slovakia 0.001% (5e-04% - 0.003%) 1,541 21 5,459,643
Slovenia 0.0076% (0.0025% - 0.023%) 1,488 15 2,078,932
Somalia 0.013% (0.006% - 0.037%) 2,513 685 15,893,219
South Africa 0.27% (0.15% - 0.56%) 58,568 29,328 59,308,690
South Korea 0.0029% (0.0014% - 0.0083%) 12,003 562 51,269,183
South Sudan 0.014% (0.0071% - 0.037%) 1,604 610 11,193,729
Spain 0.022% (0.012% - 0.097%) 242,707 3,479 46,754,783
Sri Lanka 0.0032% (0.0019% - 0.0069%) 1,877 319 21,413,250
Sudan 0.059% (0.027% - 0.14%) 6,730 2,209 43,849,269
Sweden 0.72% (0.38% - 1.5%) 48,288 11,812 10,099,270
Switzerland 0.016% (0.0074% - 0.039%) 30,961 216 8,654,618
Tajikistan 0.027% (0.017% - 0.049%) 4,834 1,271 9,537,642
Thailand 0.00018% (9.1e-05% - 0.00049%) 3,125 49 69,799,978
Togo 0.0028% (0.0015% - 0.0082%) 524 96 8,278,737
Tunisia 0.00052% (0.00018% - 0.0023%) 1,087 16 11,818,618
Turkey 0.035% (0.019% - 0.072%) 174,023 11,903 84,339,067
Ukraine 0.064% (0.031% - 0.15%) 29,070 5,866 43,733,759
United Arab Emirates 0.16% (0.1% - 0.3%) 40,986 7,816 9,890,400
United Kingdom 0.45% (0.24% - 0.92%) 291,409 20,187 67,886,004
United Republic of Tanzania 0% (0% - 0%) 509 0 59,734,213
United States of America 0.44% (0.24% - 0.89%) 2,023,347 276,260 331,002,647
Uruguay 0.0033% (0.0012% - 0.011%) 847 31 3,473,727
Uzbekistan 0.0081% (0.0051% - 0.016%) 4,819 1,306 33,469,199
Venezuela 0.011% (0.0066% - 0.023%) 2,814 1,445 28,435,943
Yemen 0.049% (0.022% - 0.11%) 591 304 29,825,968

Table 1: Estimates for the prevalence of COVID-19 in each country with greater than 10 deaths. We use the under-reporting estimates to adjust the reported case curves and tally these up over the last ten days as a proxy for prevalence. See Detailed Methods for more details.

Adjusted symptomatic case estimates

Figure 2: Estimated number of new symptomatic cases, calculated using our temporal under-reporting estimates. We adjust the reported case numbers each day - for each country with an under-reporting estimate - using our temporal under-reporting estimates to arrive at an estimate of the true number of symptomatic cases each day. The shaded blue region represents the 95% CrI, calcuated directly using the 95% CrI of the temporal under-reporting estimate.

Reported cases

Figure 3: Reported number of cases each day, pulled from the ECDC and plotted against time for comparison with our estimated true numbers of symptomatic cases each day, adjusted using our under-reporting estimates.

Under-reporting estimates to-date in table form

Country Percentage of symptomatic cases reported (95% CI) Total cases Total deaths
Afghanistan 42% (30%-56%) 28,424 569
Albania 84% (38%-100%) 1,891 43
Algeria 16% (12%-22%) 11,631 837
Andorra 34% (17%-74%) 855 52
Argentina 55% (43%-68%) 41,191 992
Armenia 60% (46%-76%) 19,708 332
Australia 91% (68%-100%) 7,436 102
Austria 38% (19%-67%) 17,247 688
Azerbaijan 81% (61%-99%) 12,238 148
Bahrain 97% (86%-100%) 21,331 60
Bangladesh 88% (70%-100%) 108,775 1,425
Belarus 98% (80%-100%) 57,936 343
Belgium 33% (25%-43%) 60,550 9,696
Bolivia 36% (28%-44%) 23,512 740
Bosnia and Herzegovina 50% (23%-91%) 3,288 169
Brazil 32% (25%-38%) 1,067,579 49,976
Bulgaria 21% (15%-28%) 3,872 199
Burkina Faso 61% (26%-98%) 902 53
Cameroon 86% (46%-100%) 11,281 300
Canada 26% (21%-32%) 101,008 8,410
Chad 58% (18%-97%) 858 74
Chile 61% (55%-68%) 236,748 4,295
China 31% (12%-98%) 84,553 4,639
Colombia 27% (22%-33%) 65,633 2,126
Congo 49% (27%-83%) 1,013 28
Costa Rica 91% (67%-100%) 2,127 12
Cote dIvoire 95% (79%-100%) 6,874 49
Croatia 23% (9.3%-46%) 2,299 107
Cuba 82% (46%-100%) 2,309 85
Cyprus 80% (33%-100%) 985 19
Czechia 74% (47%-99%) 10,448 336
Democratic Republic of the Congo 69% (43%-96%) 5,671 124
Denmark 62% (39%-86%) 12,391 600
Djibouti 92% (72%-100%) 4,565 45
Dominican Republic 60% (39%-89%) 25,778 655
Ecuador 15% (12%-19%) 50,183 4,199
Egypt 21% (17%-26%) 53,758 2,106
El Salvador 53% (36%-72%) 4,626 98
Equatorial Guinea 91% (67%-100%) 1,664 32
Estonia 45% (26%-81%) 1,981 69
Ethiopia 47% (33%-66%) 4,469 72
Finland 84% (53%-100%) 7,142 326
France 30% (24%-38%) 160,093 29,633
Gabon 94% (75%-100%) 4,428 34
Georgia 79% (43%-100%) 898 14
Germany 43% (32%-55%) 189,822 8,882
Ghana 97% (83%-100%) 13,711 85
Greece 24% (15%-38%) 3,256 190
Guatemala 35% (26%-47%) 12,755 514
Guinea 96% (84%-100%) 4,960 27
Guinea Bissau 65% (31%-99%) 1,541 17
Guyana 56% (18%-99%) 183 12
Haiti 84% (52%-100%) 5,077 88
Honduras 44% (32%-57%) 12,250 358
Hungary 14% (9.1%-20%) 4,094 570
Iceland 86% (47%-100%) 1,822 10
India 18% (15%-22%) 410,461 13,254
Indonesia 23% (18%-28%) 45,029 2,429
Iran 33% (27%-40%) 202,584 9,507
Iraq 19% (15%-24%) 29,222 1,013
Ireland 25% (16%-36%) 25,374 1,715
Israel 87% (69%-100%) 20,633 305
Italy 11% (9.2%-14%) 238,275 34,610
Jamaica 67% (22%-100%) 657 10
Japan 46% (29%-67%) 17,799 952
Jersey 22% (7.6%-64%) 318 31
Kazakhstan 32% (17%-58%) 17,225 118
Kenya 47% (31%-67%) 4,478 121
Kosovo 77% (46%-100%) 1,486 33
Kuwait 98% (89%-100%) 39,145 319
Kyrgyzstan 83% (53%-100%) 3,151 37
Latvia 32% (8.9%-61%) 1,111 30
Lebanon 81% (47%-100%) 1,536 32
Liberia 38% (16%-79%) 601 33
Lithuania 30% (17%-48%) 1,795 76
Luxembourg 57% (36%-87%) 4,105 110
Malaysia 97% (76%-100%) 8,556 121
Mali 27% (18%-42%) 1,933 109
Mauritania 25% (16%-38%) 2,813 108
Mexico 9% (7.4%-11%) 175,202 20,781
Moldova 32% (25%-40%) 13,953 464
Morocco 97% (79%-100%) 9,801 213
Nepal 97% (87%-100%) 8,605 22
Netherlands 49% (34%-66%) 49,502 6,089
Nicaragua 72% (25%-100%) 2,014 64
Niger 29% (12%-71%) 1,035 67
Nigeria 47% (35%-61%) 19,808 506
North Macedonia 22% (16%-28%) 5,005 233
Norway 76% (32%-100%) 8,708 244
Oman 98% (90%-100%) 28,566 128
Pakistan 57% (47%-68%) 176,617 3,501
Panama 71% (55%-87%) 25,222 493
Paraguay 93% (70%-100%) 1,362 13
Peru 31% (25%-37%) 251,338 7,861
Philippines 80% (62%-96%) 29,400 1,150
Poland 35% (25%-46%) 31,620 1,346
Portugal 79% (62%-93%) 38,841 1,528
Puerto Rico 95% (81%-100%) 6,463 147
Qatar 82% (31%-100%) 86,488 94
Romania 18% (12%-25%) 23,730 1,500
Russia 51% (44%-57%) 576,952 8,002
San Marino 84% (30%-100%) 696 42
Sao Tome and Principe 85% (53%-100%) 698 12
Saudi Arabia 56% (45%-68%) 154,233 1,230
Senegal 74% (37%-99%) 5,738 82
Serbia 94% (67%-100%) 12,803 260
Sierra Leone 79% (47%-100%) 1,309 53
Singapore 88% (49%-100%) 41,833 26
Slovakia 76% (44%-100%) 1,586 28
Slovenia 22% (11%-49%) 1,519 109
Somalia 77% (49%-98%) 2,755 88
South Africa 51% (41%-62%) 92,681 1,877
South Korea 84% (53%-100%) 12,421 280
South Sudan 66% (39%-97%) 1,864 34
Sri Lanka 94% (74%-100%) 1,950 11
Sudan 25% (18%-35%) 8,580 521
Sweden 41% (32%-52%) 56,043 5,053
Switzerland 39% (26%-57%) 31,243 1,680
Tajikistan 99% (86%-100%) 5,399 52
Thailand 79% (48%-100%) 3,147 58
Togo 84% (51%-100%) 561 13
Tunisia 47% (16%-97%) 1,156 50
Turkey 86% (70%-98%) 186,493 4,927
Ukraine 37% (27%-49%) 35,825 994
United Arab Emirates 97% (79%-100%) 44,533 301
United Kingdom 18% (14%-21%) 303,110 42,589
United States of America 51% (43%-59%) 2,255,119 119,719
Uruguay 51% (25%-92%) 859 25
Uzbekistan 97% (86%-100%) 6,216 19
Yemen 2.7% (2%-3.5%) 923 254
Zambia 76% (39%-100%) 1,430 11

Table 2: Estimates for the proportion of symptomatic cases reported in different countries using cCFR estimates based on case and death timeseries data from the ECDC. Total cases and deaths in each country is also shown. Confidence intervals calculated using an exact binomial test with 95% significance.

Adjusting for outcome delay in CFR estimates

During an outbreak, the naive CFR (nCFR), i.e. the ratio of reported deaths date to reported cases to date, will underestimate the true CFR because the outcome (recovery or death) is not known for all cases [5]. We can therefore estimate the true denominator for the CFR (i.e. the number of cases with known outcomes) by accounting for the delay from confirmation-to-death [1].

We assumed the delay from confirmation-to-death followed the same distribution as estimated hospitalisation-to-death, based on data from the COVID-19 outbreak in Wuhan, China, between the 17th December 2019 and the 22th January 2020, accounting right-censoring in the data as a result of as-yet-unknown disease outcomes (Figure 1, panels A and B in [7]). The distribution used is a Lognormal fit, has a mean delay of 13 days and a standard deviation of 12.7 days [7].

To correct the CFR, we use the case and death incidence data to estimate the proportion of cases with known outcomes [1,6]:

\[ u_{t} = \frac{ \sum_{j = 0}^{t} c_{t-j} f_j}{c_t}, \]

where \(u_t\) represents the underestimation of the proportion of cases with known outcomes [1,5,6] and is used to scale the value of the cumulative number of cases in the denominator in the calculation of the cCFR, \(c_{t}\) is the daily case incidence at time, \(t\) and \(f_t\) is the proportion of cases with delay of \(t\) between confirmation and death.

Approximating the proportion of symptomatic cases reported

At this stage, raw estimates of the CFR of COVID-19 correcting for delay to outcome, but not under-reporting, have been calculated. These estimates range between 1% and 1.5% [1–3]. We assume a CFR of 1.4% (95% CrI: 1.2-1.7%), taken from a recent large study [3], as a baseline CFR. We use it to approximate the potential level of under-reporting in each country. Specifically, we perform the calculation \(\frac{1.4\%}{\text{cCFR}}\) of each country to estimate an approximate fraction of cases reported.

Temporal variation model fitting

We estimate the level of under-reporting on every day for each country that has had more than ten deaths. We then fit a Gaussian Process (GP) model using the library greta and greta.gp. The parameters we fit and their priors are the following: \[ \begin{aligned} &\sigma \sim \text{Log Normal(-1, 1)}: \quad &\text{Variance of the reporting kernel} \\ &\text{L} \sim \text{Log Normal(4, 0.5)}: \quad &\text{Lengthscale of the reporting kernel} \\ &\sigma_{\text{obs}} \sim \text{Truncated Normal(0, 0.5)}, \quad &\text{Variance of the obseration kernel, truncated at 0} \end{aligned} \] The kernel is split into two components: the reporting kernel \(R\), and the observation kernel \(O\). The reporting component has a standard squared-exponential form. For the observation component, we use an i.i.d. noise kernel to acccount for observation overdispersion, which can smooth out overly clumped death time-series. This is important as some countries have been known to report an unusually large number of deaths on a single day, due to past under-reporting.

In the sampling and fitting process, we calculate the expected number of deaths at each time-point, given the baseline CFR. We then use a Poisson likelihood, where the expected number of deaths is the rate of the Poisson likelihood, given the observed number of deaths

Approximating prevalence

We use the adjusted case curves, adjusted for under-reporting and for asymptomatic infections as a proxy for prevalence. Specifically, we tally up the adjusted new cases each day over the last ten days and calculate what percentage of the population in question this total equates to. This serves as a crude prevalence estimate. We assume ten days of infectiousness as taken from the mean of the total infectious period [8].

Adjusting case counts for under-reporting

We adjust the reported number of cases each day, pulled from the ECDC. Specifically, we divide the case numbers of each day by our “proportion of cases reported” estimates that we calculate each day for each country.*

Limitations

Implicit in assuming that the under-reporting is \(\frac{1.4\%}{\text{cCFR}}\) for a given country is that the deviation away from the assumed 1.4% CFR is entirely down to under-reporting. In reality, burden on healthcare system is a likely contributing factor to higher than 1.4% CFR estimates, along with many other country specific factors.

The following is a list of the other prominent assumptions made in our analysis:

Code and data availability

The code is publically available at https://github.com/thimotei/CFR_calculation. The data required for this analysis is a time-series for both cases and deaths, along with the corresponding delay distribution. We scrape this data from ECDC, using the NCoVUtils package [9].

The under-reporting estimates for all countries can be downloaded as a single .csv file here.

Similarly, global prevalence estimates can be downloaded as a single .csv file here

Acknowledgements

The authors, on behalf of the Centre for the Mathematical Modelling of Infectious Diseases (CMMID) COVID-19 working group, wish to thank DSTL for providing the High Performance Computing facilities and associated expertise that has enabled these models to be prepared, run and processed and in an appropriately-rapid and highly efficient manner.

References

1 Russell TW, Hellewell J, Jarvis CI et al. Estimating the infection and case fatality ratio for covid-19 using age-adjusted data from the outbreak on the diamond princess cruise ship. medRxiv 2020.

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3 Guan W-j, Ni Z-y, Hu Y et al. Clinical characteristics of coronavirus disease 2019 in china. New England Journal of Medicine 2020.

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